rankZ: Rank Z transformation
In dmgatti/DOQTL: Genotyping and QTL Mapping in DO Mice
rankZ R Documentation
Rank Z transformation
Description
This is also called the inverse normal transformation. We rank the data in x, divide by n-1, and take quantiles from the normal distribution using qnorm.
Usage
rankZ(x)
Arguments
x
Numeric vector of values to be transformed.
Details
We often use this when there are hundreds or thousands of phenotypes. This allows us to calculate permutation derived thresholds once and use them for all phenotypes.
Value
Numeric vector with a normal distribution.
Author(s)
Daniel Gatti
Examples
rankZ(rlnorm(20))
dmgatti/DOQTL documentation built on April 7, 2024, 10:35 p.m.
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| rankZ | R Documentation |
Rank Z transformation
Description
This is also called the inverse normal transformation. We rank the data in x, divide by n-1, and take quantiles from the normal distribution using qnorm.
Usage
rankZ(x)
Arguments
x |
Numeric vector of values to be transformed. |
Details
We often use this when there are hundreds or thousands of phenotypes. This allows us to calculate permutation derived thresholds once and use them for all phenotypes.
Value
Numeric vector with a normal distribution.
Author(s)
Daniel Gatti
Examples
rankZ(rlnorm(20))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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